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Add smoothing parameter for compatibility with BayesNet

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This commit is contained in:
Ricardo Montañana Gómez
2024-06-11 13:43:12 +02:00
parent 235c345e87
commit c5ff1a0b2b
3 changed files with 7 additions and 7 deletions
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2
CMakeLists.txt
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@@ -1,7 +1,7 @@
cmake_minimum_required(VERSION 3.20) cmake_minimum_required(VERSION 3.20)
project(PyClassifiers project(PyClassifiers
VERSION 1.0.1 VERSION 1.0.2
DESCRIPTION "Python Classifiers Wrapper." DESCRIPTION "Python Classifiers Wrapper."
HOMEPAGE_URL "https://github.com/rmontanana/pyclassifiers" HOMEPAGE_URL "https://github.com/rmontanana/pyclassifiers"
LANGUAGES CXX LANGUAGES CXX

2
pyclfs/PyClassifier.cc
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@@ -70,7 +70,7 @@ namespace pywrap {
fitted = true; fitted = true;
return *this; return *this;
} }
PyClassifier& PyClassifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) PyClassifier& PyClassifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing)
{ {
return fit(X, y); return fit(X, y);
} }

10
pyclfs/PyClassifier.h
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@@ -17,12 +17,12 @@ namespace pywrap {
public: public:
PyClassifier(const std::string& module, const std::string& className, const bool sklearn = false); PyClassifier(const std::string& module, const std::string& className, const bool sklearn = false);
virtual ~PyClassifier(); virtual ~PyClassifier();
PyClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override { return *this; }; PyClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override { return *this; };
// X is nxm tensor, y is nx1 tensor // X is nxm tensor, y is nx1 tensor
PyClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override; PyClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override;
PyClassifier& fit(torch::Tensor& X, torch::Tensor& y); PyClassifier& fit(torch::Tensor& X, torch::Tensor& y);
PyClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override { return *this; }; PyClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override { return *this; };
PyClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights) override { return *this; }; PyClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override { return *this; };
torch::Tensor predict(torch::Tensor& X) override; torch::Tensor predict(torch::Tensor& X) override;
std::vector<int> predict(std::vector<std::vector<int >>& X) override { return std::vector<int>(); }; // Not implemented std::vector<int> predict(std::vector<std::vector<int >>& X) override { return std::vector<int>(); }; // Not implemented
torch::Tensor predict_proba(torch::Tensor& X) override { return torch::zeros({ 0, 0 }); } // Not implemented torch::Tensor predict_proba(torch::Tensor& X) override { return torch::zeros({ 0, 0 }); } // Not implemented
@@ -47,7 +47,7 @@ namespace pywrap {
void setHyperparameters(const nlohmann::json& hyperparameters) override; void setHyperparameters(const nlohmann::json& hyperparameters) override;
protected: protected:
nlohmann::json hyperparameters; nlohmann::json hyperparameters;
void trainModel(const torch::Tensor& weights) override {}; void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override {};
std::vector<std::string> notes; std::vector<std::string> notes;
private: private:
PyWrap* pyWrap; PyWrap* pyWrap;